no code implementations • 7 Sep 2024 • Hemanth Kandula, Damianos Karakos, Haoling Qiu, Benjamin Rozonoyer, Ian Soboroff, Lee Tarlin, Bonan Min
We present a novel, interactive system called $\textit{QueryBuilder}$, which allows a novice, English-speaking user to create queries with a small amount of effort, through efficient exploration of an English development corpus in order to rapidly develop cross-lingual information retrieval queries corresponding to the user's information needs.
Cross-Lingual Information Retrieval Efficient Exploration +1
no code implementations • LREC 2020 • Le Zhang, Damianos Karakos, William Hartmann, Manaj Srivastava, Lee Tarlin, David Akodes, Sanjay Krishna Gouda, Numra Bathool, Lingjun Zhao, Zhuolin Jiang, Richard Schwartz, John Makhoul
In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English.
no code implementations • LREC 2020 • Richard Schwartz, John Makhoul, Lee Tarlin, Damianos Karakos
We describe the human triage scenario envisioned in the Cross-Lingual Information Retrieval (CLIR) problem of the [REDUCT] Program.
no code implementations • LREC 2020 • Damianos Karakos, Rabih Zbib, William Hartmann, Richard Schwartz, John Makhoul
In the IARPA MATERIAL program, information retrieval (IR) is treated as a hard detection problem; the system has to output a single global ranking over all queries, and apply a hard threshold on this global list to come up with all the hypothesized relevant documents.
1 code implementation • LREC 2020 • Zhuolin Jiang, Amro El-Jaroudi, William Hartmann, Damianos Karakos, Lingjun Zhao
Multiple neural language models have been developed recently, e. g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking.
no code implementations • WS 2019 • Lingjun Zhao, Rabih Zbib, Zhuolin Jiang, Damianos Karakos, Zhongqiang Huang
We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages.